摘要
在概率感知优化模型下,将无线传感器网络(WSNs)的覆盖率和移动节点的能耗作为多目标优化函数,通过改进混合粒子群优化算法(IM-HPSO)不断迭代,调整移动节点的最优位置,控制网络覆盖率最大化,同时减小移动距离,使得能耗最小化。仿真结果表明:IM-HPSO算法在覆盖率的提高、能耗的减少、网络生命周期的延长方面优于其他算法。
In probabilistic perceptual optimization model,coverage rate of the wireless sensor networks（ WSNs）and energy consumption of mobile node are used as multi-objective optimization function,and the optimal location of the mobile node is adjusted by improved hybrid particle swarm optimization（ IM-HPSO） algorithm to control the network coverage rate maximization,at the same time,reducing moving distance,making the mobile energy consumption minimized. The simulation results show that the IM-HPSO algorithm outperforms the other algorithms in terms of coverage rate decrease of energy consumption and the extension of network lifetime.
作者
朱正伟
刁小敏
郭晓
刘晨
ZHU Zheng-wei;DIAO Xiao-min;GUO Xiao;LIU Chen(College of Information Science and Engineering, Changzhon University, Changzhou 213164, China)
出处
《传感器与微系统》
CSCD
2018年第6期150-152,157,共4页
Transducer and Microsystem Technologies
基金
江苏省常州市应用基础研究项目(CJ20159035)
关键词
移动节点
改进混合粒子群优化算法
覆盖率
能耗
网络生命周期
mobile node
improved hybrid particle swarm optimization （IM-HPSO）algorithm
coverage rate
energy consumption
network life cycle
